Rethinking drug design in the artificial intelligence era

Abstract

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the ‘grand challenges’ in small-molecule drug discovery with AI and the approaches to address them.

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Fig. 1: Integrating mind and machine in drug discovery.

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Acknowledgements

This article is based on a meeting involving a group of international experts from diverse scientific backgrounds and institutions convened in San Francisco in December 2018 for a workshop organized by the RETHINK think-and-do tank of ETH Zurich to rethink drug design with artificial intelligence. Figure 1 was created and contributed by Jack Burgess, who also acted as a visual scribe during the workshop. Jürg Brunnschweiler and the ETH Global team are thanked for excellent organizational support. This research was financially supported by the RETHINK initiative of ETH Zurich.

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All authors contributed equally to the content and approved the final version of the manuscript.

Correspondence to Gisbert Schneider.

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Competing interests

G.S. and P.S. declare a potential financial conflict of interest in their role as life science industry consultants and cofounders of inSili.com GmbH, Zurich. The remaining authors declare no competing interests.

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Related links

Click2Drug: https://www.click2drug.org/

The ATOM Consortium: https://www.atomscience.org

The Innovative Medicines Initiative: https://www.imi.europa.eu/

The SALT Knowledge Share Consortium: https://www.medchemica.com/the-salt-knowledge-share-consortium/

Glossary

Adaptive algorithm

An adaptive algorithm implements a problem-solving heuristic that changes its behaviour at the time it is run, based on information available and a reward mechanism.

Artificial intelligence

(AI). The various definitions and interpretations of this term agree on three essential capabilities of an AI (most often referring to a computer or machine): (i) problem solving, (ii) learning from experience (memory and adaptation) and (iii) coping with new challenges (generalization).

Deep learning

A set of machine learning techniques that utilize multi-layer neural networks to derive relationships from data, specifically the use of neural networks (see below) with many layers. Neural networks with many layers are called ‘deep neural networks’, which corresponds to having many layers of function compositions. Typically, the deeper the layer, the more abstract the semantics of its ‘feature space’ (that is, the implicit representation created by the neural network at that layer).

Hypothesis

A supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation, without any assumption of its truth. In the context of drug design, a molecular structure can serve as a hypothesis.

Machine learning

The science (and art) of programming computers so that they can learn from data; also a branch of artificial intelligence focused on one of several tasks, typically all function approximators. The most common task is the construction and training of classifier models, followed by regression models — both forms of ‘supervised learning’, wherein pairs of ‘inputs’ and ‘labels’ are used to train the model to then make label predictions for cases where only the inputs are observed. Also common in machine learning is ‘unsupervised learning’, wherein only ‘inputs’ are used (for example, a list of molecules numerically encoded such as by way of SMILES strings) and general properties of these are learned by the model, which can then tell you how likely a new input is to have belonged to this set of objects, or can be used to generate ‘new’ such objects. More nuanced mixing and matching of tasks is also possible, yielding ‘semi-supervised learning’.

Natural language processing

(NLP). NLP is concerned with the interactions between computers and human (natural) languages, in particular how to process and analyse large amounts of natural language data, for example, scientific literature. Deep statistical machine learning models achieve state-of-the-art results in many natural language tasks, for example, in language modelling and parsing. NLP can also be used for chemical language analysis and de novo design.

Neural networks

A particular type of function approximators wherein functions that predict discrete classes (classifiers) or real-values (regression models) do so by composing a series of (typically nonlinear) functions, each one converting the previous layer’s outputs into a new ‘space’. These models have been around for decades but came to prominence in the 1990s when the combination of access to large datasets, along with the ability to train ‘deep’ models (see Deep learning) and more powerful computers, enabled them to break benchmarks in computational audio and vision tasks.

Research culture

A community sharing certain practices or using a common method or exemplar, that is, speaking a common language (including formalisms and algorithms) or sharing typical instances, illustrations or exemplifications (including molecular structures).

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Schneider, P., Walters, W.P., Plowright, A.T. et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov (2019). https://doi.org/10.1038/s41573-019-0050-3

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